Information Fusion as a Means of Forecasting Expenditures for Regenerating Complex Investment Goods

Authors: Steffen C. Eickemeyer, Tim Borcherding, Peter Nyhuis, Hannover

Abstract:

Planning capacities when regenerating complex investment goods involves particular challenges in that the planning is subject to a large degree of uncertainty regarding load information. Using information fusion – by applying Bayesian Networks – a method is being developed for forecasting the anticipated expenditures (human labor, tool and machinery utilization, time etc.) for regenerating a good. The generated forecasts then later serve as a tool for planning capacities and ensure a greater stability in the planning processes.

Keywords: Bayesian networks, capacity planning, complex investment goods, damages library, forecasting, information fusion, regeneration.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070079

References:

[1] B. Scholz-Reiter, H. Scharke, "Reaktive Planung," Industrie Management, vol. 16, no. 2, pp. 21-26, Mar. 2000.
[2] Steffen C. Eickemeyer, Peter Nyhuis,"Capacity planning and coordination with fuzzy load information, "The Business Review, Cambridge,vol.16, no. 1, pp. 259-264, Dec.2010.
[3] K. Matyas, Taschenbuch Instandhaltungslogistik: Qualität und Produktivität steigern. 2nded., Munich, Vienna: Carl Hanser Verlag, 2005, p.195 f.
[4] M. Wagner, Modellbasierte Arbeitskräfteplanung für stochastische Instandhaltungsereignisse in der zivilen Luftfahrt, PhD dissertation, Technische Universität Berlin, 2009.
[5] Supply Chain Council: SCOR - Supply Chain Operations Reference Model Version 9.0. Supply Chain Council, Cypress 2008.
[6] I. Boersch, J. Heinsohn, R. Socher, Wissensverarbeitung - Eine Einf├╝hrung in die k├╝nstliche Intelligenz f├╝r Informatiker und Ingenieure. 2nd ed., Heidelberg: Elsevier GmbH Spektrum Akademischer Verlag, 2007, pp. 262-265.
[7] V. Nov├ík, I. Perfilieva, J. Mo─ìkoř, Mathematical Principles of Fuzzy Logic. Norvell (MA): Kluwer Academic Publishers, 1999, pp. 1-12.
[8] D. Graupe, Principles of Artificial and Neural Networks. 2nd ed., Singapore: World Scientific Publishing Co. Pte. Ltd., 2007, p.1.
[9] F. Bodendorf, Daten- und Wissensmanagement, 2nded., Berlin, Heidelberg: Springer Verlag, 2006, p.117 f.
[10] J. Ma, W. Liu, P. Miller, "Event Modelling and Reasoning with Uncertain Information for Distributed Sensor Networks" in: Scalable Uncertainty Management, Deshpande, A., Hunter, A., Ed. Berlin, Heidelberg: Springer-Verlag, 2010, pp.236-249.
[11] R. Büttner, Automatisierte Verhandlungen in Multi-Agenten- Systemen - Entwurf eines argumentations-basierten Mechanismus für nur imperfekt beschreibbare Verhandlungsgegenstände, PhD dissertation, Gabler Verlag, Universität Hohenheim, 2009 , p.134.
[12] R. Akerkar, Introduction to Artificial Intelligence. New Delhi: Prenctice- Hall of India Private Limited, 2005, p. 234-237.
[13] T. Koski, Noble, J.M.: Bayesian Networks - An Introduction. Chichester: 2009, p.1.
[14] E. Alpaydin, Maschinelles Lernen. Munich: Oldenbourg Wissenschaftsverlag, 2008, p.53.